Physical Sciences and Mathematics Commons^™

Pre-R: Making Health Care Healthier, Grant Ramil, Mark Corpuz, Eliot Mestre, Akshay Rangnekar, Alex Lin

Computer Engineering

The Pre-R project provides a central database of medical service fees offered by hospitals across the United States. The database is crowdsourced and all data points are provided by end users or by hospital providers themselves. The fees are searchable through a website (www.pre-r.com) and iOS and Android applications. The mobile applications also provide a way for users to capture a picture of any medical bill and upload it for analysis and entry to our database.

Air Indexing For On-Demand Xml Data Broadcast, Weiwei Sun, Rongrui Qin, Jinjin Wu, Baihua Zheng

Research Collection School Of Computing and Information Systems

XML data broadcast is an efficient way to disseminate semi-structured information in wireless mobile environments. In this paper, we propose a novel two-tier index structure to facilitate the access of XML document in an on-demand broadcast system. It provides the clients with an overall image of all the XML documents available at the server side and hence enables the clients to locate complete result sets accordingly. A pruning strategy is developed to cut down the index size and a two-tier structure is proposed to further remove any redundant information. In addition, two index distribution strategies, namely naive distribution and partial …

On Efficient Reverse Skyline Query Processing, Yunjun Gao, Qing Liu, Baihua Zheng, Gang Chen

Research Collection School Of Computing and Information Systems

Given a D-dimensional data set P and a query point q, a reverse skyline query (RSQ) returns all the data objects in P whose dynamic skyline contains q. It is important for many real life applications such as business planning and environmental monitoring. Currently, the state-of-the-art algorithm for answering the RSQ is the reverse skyline using skyline approximations (RSSA) algorithm, which is based on the precomputed approximations of the skylines. Although RSSA has some desirable features, e.g., applicability to arbitrary data distributions and dimensions, it needs for multiple accesses of the same nodes, incurring redundant I/O and CPU costs. In …

Active Learning With Efficient Feature Weighting Methods For Improving Data Quality And Classification Accuracy, Justin Martineau, Lu Chen, Doreen Cheng, Amit P. Sheth

Kno.e.sis Publications

Many machine learning datasets are noisy with a substantial number of mislabeled instances. This noise yields sub-optimal classification performance. In this paper we study a large, low quality annotated dataset, created quickly and cheaply using Amazon Mechanical Turk to crowdsource annotations. We describe computationally cheap feature weighting techniques and a novel non-linear distribution spreading algorithm that can be used to iteratively and interactively correcting mislabeled instances to significantly improve annotation quality at low cost. Eight different emotion extraction experiments on Twitter data demonstrate that our approach is just as effective as more computationally expensive techniques. Our techniques save a considerable …

Semantic Modelling Of Smart City Data, Stefan Bischof, Athanasios Karapantelakis, Cosmin-Septimiu Nechifor, Amit P. Sheth, Alessandra Mileo, Payam Barnaghi

Kno.e.sis Publications

Cities present an opportunity for rendering Web of Things-enabled services. According to the World Health Organization, population in cities will double by the middle of this century, while cities deal with increasingly pressing issues such as environmental sustainability, economic growth and citizen mobility. In this paper, we propose a discussion around the need for common semantic descriptions for smart city data to facilitate future services in "smart cities". We present examples of data that can be collected from cities, discuss issues around this data and put forward some preliminary thoughts for creating a semantic description model to describe and help …

Semantics-Enhanced Geoscience Interoperability, Analytics, And Applications, Krishnaprasad Thirunarayan, Amit P. Sheth

Kno.e.sis Publications

We present our research ideas for developing cyberinfrastructure for Geoscience applications developed in the context of the EarthCube initiative, and our NSF-sponsored work on incorporating spatial-temporal-thematic semantics for enhanced querying and feature extraction from sensor data streams.

Socio-Physical Analytics: Challenges & Opportunities, Archan Misra, Kasthuri Jayarajah, Shriguru Nayak, Philips Kokoh Prasetyo, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

In this paper, we argue for expanded research into an area called Socio-Physical Analytics, that focuses on combining the behavioral insight gained from mobile-sensing based monitoring of physical behavior with the inter-personal relationships and preferences deduced from online social networks. We highlight some of the research challenges in combining these heterogeneous data sources and then describe some examples of our ongoing work (based on real-world data being collected at SMU) that illustrate two aspects of socio-physical analytics: (a) how additional demographic and online analytics based attributes can potentially provide better insights into the preferences and behaviors of individuals or groups …

Learning Euclidean-To-Riemannian Metric For Point-To-Set Classification, Zhiwu Huang, R. Wang, S. Shan, X. Chen

Research Collection School Of Computing and Information Systems

In this paper, we focus on the problem of point-to-set classification, where single points are matched against sets of correlated points. Since the points commonly lie in Euclidean space while the sets are typically modeled as elements on Riemannian manifold, they can be treated as Euclidean points and Riemannian points respectively. To learn a metric between the heterogeneous points, we propose a novel Euclidean-to-Riemannian metric learning framework. Specifically, by exploiting typical Riemannian metrics, the Riemannian manifold is first embedded into a high dimensional Hilbert space to reduce the gaps between the heterogeneous spaces and meanwhile respect the Riemannian geometry of …

Graph-Based Semi-Supervised Learning: Realizing Pointwise Smoothness Probabilistically, Yuan Fang, Kevin Chen-Chuan Chang, Hady W. Lauw

Research Collection School Of Computing and Information Systems

As the central notion in semi-supervised learning, smoothness is often realized on a graph representation of the data. In this paper, we study two complementary dimensions of smoothness: its pointwise nature and probabilistic modeling. While no existing graph-based work exploits them in conjunction, we encompass both in a novel framework of Probabilistic Graph-based Pointwise Smoothness (PGP), building upon two foundational models of data closeness and label coupling. This new form of smoothness axiomatizes a set of probability constraints, which ultimately enables class prediction. Theoretically, we provide an error and robustness analysis of PGP. Empirically, we conduct extensive experiments to show …

An Air Index For Spatial Query Processing In Road Networks, Weiwei Sun, Chunan Chen, Baihua Zheng, Chong Chen, Peng Liu

Research Collection School Of Computing and Information Systems

Spatial queries such as range query and kNN query in road networks have received a growing number of attention in real life. Considering the large population of the users and the high overhead of network distance computation, it is extremely important to guarantee the efficiency and scalability of query processing. Motivated by the scalable and secure properties of wireless broadcast model, this paper presents an air index called Network Partition Index (NPI) to support efficient spatial query processing in road networks via wireless broadcast. The main idea is to partition the road network into a number of regions and then …

Contextual Anomaly Detection In Big Sensor Data, Michael Hayes, Miriam A M Capretz

Electrical and Computer Engineering Publications

Performing predictive modelling, such as anomaly detection, in Big Data is a difficult task. This problem is compounded as more and more sources of Big Data are generated from environmental sensors, logging applications, and the Internet of Things. Further, most current techniques for anomaly detection only consider the content of the data source, i.e. the data itself, without concern for the context of the data. As data becomes more complex it is increasingly important to bias anomaly detection techniques for the context, whether it is spatial, temporal, or semantic. The work proposed in this paper outlines a contextual anomaly detection …

Competition Policy And The Technologies Of Information, Herbert J. Hovenkamp

All Faculty Scholarship

When we speak about information and competition policy we are usually thinking about oral or written communications that have an anticompetitive potential, and mainly in the context of collusion of exclusionary threats. These are important topics. Indeed, among the most difficult problems that competition policy has had to confront over the years is understanding communications that can be construed as either threats to exclude or as offers to collude or facilitators of collusion.

My topic here, however, is the relationship between information technologies and competition policy. Technological change can both induce and undermine the use of information to facilitate anticompetitive …

Joint Virtual Machine And Bandwidth Allocation In Software Defined Network (Sdn) And Cloud Computing Environments, Jonathan David Chase, Rakpong Kaewpuang, Wen Yonggang, Dusit Niyato

Research Collection School Of Computing and Information Systems

Cloud computing provides users with great flexibility when provisioning resources, with cloud providers offering a choice of reservation and on-demand purchasing options. Reservation plans offer cheaper prices, but must be chosen in advance, and therefore must be appropriate to users' requirements. If demand is uncertain, the reservation plan may not be sufficient and on-demand resources have to be provisioned. Previous work focused on optimally placing virtual machines with cloud providers to minimize total cost. However, many applications require large amounts of network bandwidth. Therefore, considering only virtual machines offers an incomplete view of the system. Exploiting recent developments in software …

Ar-Miner: Mining Informative Reviews For Developers From Mobile App Marketplace, Ning Chen, Jialiu Lin, Steven C. H. Hoi, Xiaokui Xiao, Boshen Zhang

Research Collection School Of Computing and Information Systems

With the popularity of smartphones and mobile devices, mobile application (a.k.a. “app”) markets have been growing exponentially in terms of number of users and downloads. App developers spend considerable effort on collecting and exploiting user feedback to improve user satisfaction, but suffer from the absence of effective user review analytics tools. To facilitate mobile app developers discover the most “informative” user reviews from a large and rapidly increasing pool of user reviews, we present “AR-Miner” — a novel computational framework for App Review Mining, which performs comprehensive analytics from raw user reviews by (i) first extracting informative user reviews by …

On Modeling Brand Preferences In Item Adoptions, Minh Duc Luu, Ee Peng Lim, Freddy Chong-Tat Chua

Research Collection School Of Computing and Information Systems

In marketing and advertising, developing and managingbrands value represent the core activities performedby companies. Successful brands attract buyers andadopters, which in turn increase the companies’ value.Given a set of user-item adoption data, can we inferbrand effects from users adopting items? To answerthis question, we develop the Brand Item Topic Model(BITM) that incorporates users’ brand preferences inthe process of item adoption by the users. We evaluateour model using synthetic and two real world datasetsagainst baseline models which do not consider brand effects.The results show that BITM can determine userswho demonstrate brand preferences and predict itemadoptions more accurately.

Hydra: Large-Scale Social Identity Linkage Via Heterogeneous Behavior Modeling, Siyuan Liu, Shuhui Wang, Feida Zhu, Jinbo Zhang, Ramayya Krishnan

Research Collection School Of Computing and Information Systems

We study the problem of large-scale social identity linkage across different social media platforms, which is of critical importance to business intelligence by gaining from social data a deeper understanding and more accurate profiling of users. This paper proposes HYDRA, a solution framework which consists of three key steps: (I) modeling heterogeneous behavior by long-term behavior distribution analysis and multi-resolution temporal information matching; (II) constructing structural consistency graph to measure the high-order structure consistency on users' core social structures across different platforms; and (III) learning the mapping function by multi-objective optimization composed of both the supervised learning on pair-wise ID …

Global Immutable Region Computation, Jilian Zhang, Kyriakos Mouratidis, Hwee Hwa Pang

Research Collection School Of Computing and Information Systems

A top-k query shortlists the k records in a dataset that best match the user's preferences. To indicate her preferences, the user typically determines a numeric weight for each data dimension (i.e., attribute). We refer to these weights collectively as the query vector. Based on this vector, each data record is implicitly mapped to a score value (via a weighted sum function). The records with the k largest scores are reported as the result. In this paper we propose an auxiliary feature to standard top-k query processing. Specifically, we compute the maximal locus within which the query vector incurs no …

Recommendation Support For Multi-Attribute Databases, Jilian Zhang

Dissertations and Theses Collection (Open Access)

This dissertation studies the subject of providing recommendation support for multi-attribute databases. Recommendation is an important and very useful information evaluation mechanism that explores a database of huge volume, and retrieves from it the interesting data items (tuples) for users based on their preferences.

Defy: A Deniable File System For Flash Memory, Timothy M. Peters

Master's Theses

While solutions for file system encryption can prevent an adversary from determining the contents of files, in situations where a user wishes to hide even the existence of data, encryption alone is not enough. Indeed, encryption may draw attention to those files, as they most likely contain information the user wishes to keep secret, and coercion can be a very strong motivator for the owner of an encrypted file system to surrender their secret key.

Herein we present DEFY, a deniable file system designed to work exclusively with solid-state drives, particularly those found in mobile devices. Solid-state drives have unique …

Online Community Transition Detection, Biying Tan, Feida Zhu, Qiang Qu, Siyuan Liu

Research Collection School Of Computing and Information Systems

Mining user behavior patterns in social networks is of great importance in user behavior analysis, targeted marketing, churn prediction and other applications. However, less effort has been made to study the evolution of user behavior in social communities. In particular, users join and leave communities over time. How to automatically detect the online community transitions of individual users is a research problem of immense practical value yet with great technical challenges. In this paper, we propose an algorithm based on the Minimum Description Length (MDL) principle to trace the evolution of community transition of individual users, adaptive to the noisy …

Analyzing Blackboard: Using A Learning Management System From The Student Perspective, Jennifer Squillante, Lekia Wise, Thomas Hartey

Mathematics and Computer Science Capstones

This report was provided to gain insight into the student perspective on how students interact with their current Learning Management System (LMS), Blackboard. It is currently used to house course content for La Salle’s traditional, online, and hybrid (combination of traditional and online sessions) courses. The university is currently investigating on whether or not there are advantages to switching to an alternate LMS and wanted to gather information on the current student opinion of the tool.

The research shows that La Salle’s student population did not favor one LMS tool over another but the research did show which features were …

Web Portal For Elderly Patients To Research Prescription Medication, Krista Miller, Robert File, Wil Murphy Jr

Mathematics and Computer Science Capstones

People 55 and older constitute 25.2% of the U.S. population. These senior citizens use a vast amount of prescription drugs to treat and control health related issues. An analysis of the drug industry reveals several challenges facing this demographic group: costs, coverage by insurance companies and drug interactions. The need for a centrally located drug information source for senior citizens and their caregivers is immense. We propose researching and viewing what is available from various areas and developing a user-friendly information source prototype to assist this demographic group with their medication under takings. We expect to deliver a summary of …

Hydrographic Surface Modeling Through A Raster Based Spline Creation Method, Julie G. Alexander

University of New Orleans Theses and Dissertations

The United States Army Corp of Engineers relies on accurate and detailed surface models for various construction projects and preventative measures. To aid in these efforts, it is necessary to work for advancements in surface model creation. Current methods for model creation include Delaunay triangulation, raster grid interpolation, and Hydraulic Spline grid generation. While these methods produce adequate surface models, attempts for improved methods can still be made.

A method for raster based spline creation is presented as a variation of the Hydraulic Spline algorithm. By implementing Hydraulic Splines in raster data instead of vector data, the model creation process …

Lexical Based Semantic Orientation Of Online Customer Reviews And Blogs-J-Am Sci 10(8) 143_147--07-June-2014.Pdf, Dr. Muhammad Zubair Asghar

Dr. Muhammad Zubair Asghar

Rapid increase in internet users along with growing power of online review sites and social media hasgiven birth to sentiment analysis or opinion mining, which aims at determining what other people think andcomment. Sentiments or Opinions contain public generated content about products, services, policies and politics.People are usually interested to seek positive and negative opinions containing likes and dislikes, shared by users forfeatures of particular product or service. This paper proposed sentence-level lexical based domain independentsentiment classification method for different types of data such as reviews and blogs. The proposed method is basedon general lexicons i.e. WordNet, SentiWordNet and user …

Principles Of Incident Response And Disaster Recovery, Michael Whitman, Herbert Mattord

Herbert J. Mattord

Are you ready to respond to an unauthorized intrusion to your computer network or server? Principles of Incident Response and Disaster Recovery presents methods to identify vulnerabilities and take appropriate countermeasures to prevent and mitigate failure risks for an organization. Not only does book present a foundation in disaster recovery principles and planning, but it also emphasizes the importance of incident response minimizing prolonged downtime that can potentially lead to irreparable loss. This book is the first of its kind to address the overall problem of contingency planning rather than focusing on specific tasks of incident response or disaster recovery.

Management Of Information Security, 1st Edition, Michael Whitman, Herbert Mattord

Herbert J. Mattord

Management of Information Security is designed for senior and graduate-level business and information systems students who want to learn the management aspects of information security. This text takes a "view from the top" and presents important information for future managers regarding information security. The material covered in this text is often part of a capstone course in an information security.

Management Of Information Security, 2nd Edition, Michael Whitman, Herbert Mattord

Herbert J. Mattord

Information security-driven topic coverage is the basis for this updated book that will benefit readers in the information technology and business fields alike. Management of Information Security, provides an overview of information security from a management perspective, as well as a thorough understanding of the administration of information security. Written by two Certified Information Systems Security Professionals (CISSP), this book has the added credibility of incorporating the CISSP Common Body of Knowledge (CBK), especially in the area of information security management. The second edition has been updated to maintain the industry currency and academic relevance that made the previous edition …

Guide To Firewalls And Network Security: Intrusion Detection And Vpns, 2nd Edition, Michael Whitman, Herbert Mattord, Richard Austin, Greg Holden

Herbert J. Mattord

Firewalls are among the best-known security tools in use today, and their critical role in information security continues to grow. However, firewalls are most effective when they are backed by effective security planning, a well-designed security policy, and when they work in concert with anti-virus software, intrusion detection systems, and other tools. This book aims to explore firewalls in the context of these other elements, providing readers with a solid, in-depth introduction to firewalls that focuses on both managerial and technical aspects of security. Coverage includes packet filtering, authentication, proxy servers, encryption, bastion hosts, virtual private networks (VPNs), log file …

Principles Of Information Security, 3rd Edition, Michael Whitman, Herbert Mattord

Herbert J. Mattord

Explore the field of information security and assurance with this valuable resource that focuses on both the managerial and technical aspects of the discipline. Principles of Information Security, Third Edition builds on internationally recognized standards and bodies of knowledge to provide the knowledge and skills that information systems students need for their future roles as business decision-makers. Coverage includes key knowledge areas of the CISSP (Certified Information Systems Security Professional), as well as risk management, cryptography, physical security, and more. The third edition has retained the real-world examples and scenarios that made previous editions so successful, but has updated the …

Management Of Information Security, 3rd Edition, Michael Whitman, Herbert Mattord

Herbert J. Mattord

Management of Information Security, Third Edition focuses on the managerial aspects of information security and assurance. Topics covered include access control models, information security governance, and information security program assessment and metrics. Coverage on the foundational and technical components of information security is included to reinforce key concepts. This new edition includes up-to-date information on changes in the field such as revised sections on national and international laws and international standards like the ISO 27000 series. With these updates, Management of Information Security continues to offer a unique overview of information security from a management perspective while maintaining a finger …